Bibliography:
Aqib Niaz Bhat, Girish Kasiviswanathan, Christy Maria Mathew, Seth Polsley, Erik Prout, Daniel W. Goldberg, and Tracy Hammond. An Intelligent Sketching Interface for Education using GeographicInformation Systems, Frontiers in Pen and Touch, Chapter 11, Springer 2017.
Summary:
This paper is about a sketch recognition system that helps with
education, in the field of geography. The motivation for such a system
is that geographical enitities learnt by drawing on a map would aid in
better recall and comprehension of the various concepts. Current
lessons and grading in the field rely on marking on maps and multiple
choice tests. The main idea is to combine shape and location
information from the sketch and compare that with an actual data set
of geographic features. The initial version of the system works by
allowing students to draw rivers on maps and returns a similarity
score between their sketch and the actual geographical data.
The trade-off between drawing freedom and ease of recognition makes it
challenging to design a robust recognizer. The recognizer has to be
stroke independent, receptive to messy sketches that capture essential
information and be able to identify different drawing styles. The
recognition method used by the system, combined two techniques, shape
context and Hauseldorf distance, that exploit shape and locationn
features of the sketch. The system also includes pa preprocessing step,
that will remove sketches that are 'very far' from the actual
data. This is done by having stroke-length and location thresholds on
the sketch, in comparison to the river's geographical data.
The similarity measure is a weighted some of shape similarity measured
using shape context, location similarity measured using Hausdorff
distance and stroke length ratio. Shape context is calculated for each
point in a shape by measuring its relative distribution with respect
to the other points. A matching cost is between the 2 shapes, by
pairwise matching of the shape-context of individual points.
A modified version of Hausdorff distance is used for finding the
location similarity.
The paper concludes by talking about the user study and accuracy of
the model for both similar and dissimilar cases. Users of the system
were satisfied with both the learning and testing mode of the UI. The
authors look to expand their system to other geographic entities. They
are also looking to improve their classifier and build more
classifiers that include domain specific heuristics.
Discussion:
This system uses technology to solve a problem, that in my opinion,
not many people would think about. Sketching is a great way to learn
geography, and getting immediate feedback on your sketches makes it
very useful to learn by doing. The paper does a great job of
explaining limitations in existing methods and how they had to come up
with clever variations and new techniques, in order to apply it for
geographical matching.
I would love to see this extended for other entities, not just in
geography, but also in fields like of astronomy, history, archeology.
Aqib Niaz Bhat, Girish Kasiviswanathan, Christy Maria Mathew, Seth Polsley, Erik Prout, Daniel W. Goldberg, and Tracy Hammond. An Intelligent Sketching Interface for Education using GeographicInformation Systems, Frontiers in Pen and Touch, Chapter 11, Springer 2017.
Summary:
This paper is about a sketch recognition system that helps with
education, in the field of geography. The motivation for such a system
is that geographical enitities learnt by drawing on a map would aid in
better recall and comprehension of the various concepts. Current
lessons and grading in the field rely on marking on maps and multiple
choice tests. The main idea is to combine shape and location
information from the sketch and compare that with an actual data set
of geographic features. The initial version of the system works by
allowing students to draw rivers on maps and returns a similarity
score between their sketch and the actual geographical data.
The trade-off between drawing freedom and ease of recognition makes it
challenging to design a robust recognizer. The recognizer has to be
stroke independent, receptive to messy sketches that capture essential
information and be able to identify different drawing styles. The
recognition method used by the system, combined two techniques, shape
context and Hauseldorf distance, that exploit shape and locationn
features of the sketch. The system also includes pa preprocessing step,
that will remove sketches that are 'very far' from the actual
data. This is done by having stroke-length and location thresholds on
the sketch, in comparison to the river's geographical data.
The similarity measure is a weighted some of shape similarity measured
using shape context, location similarity measured using Hausdorff
distance and stroke length ratio. Shape context is calculated for each
point in a shape by measuring its relative distribution with respect
to the other points. A matching cost is between the 2 shapes, by
pairwise matching of the shape-context of individual points.
A modified version of Hausdorff distance is used for finding the
location similarity.
The paper concludes by talking about the user study and accuracy of
the model for both similar and dissimilar cases. Users of the system
were satisfied with both the learning and testing mode of the UI. The
authors look to expand their system to other geographic entities. They
are also looking to improve their classifier and build more
classifiers that include domain specific heuristics.
Discussion:
This system uses technology to solve a problem, that in my opinion,
not many people would think about. Sketching is a great way to learn
geography, and getting immediate feedback on your sketches makes it
very useful to learn by doing. The paper does a great job of
explaining limitations in existing methods and how they had to come up
with clever variations and new techniques, in order to apply it for
geographical matching.
I would love to see this extended for other entities, not just in
geography, but also in fields like of astronomy, history, archeology.
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